Enhanced Facial Recognition Techniques for Masked Individuals Amid the COVID-19 Pandemic
Bibliographic record
Abstract
The use of facemasks has been recommended by the World Health Organization (WHO) as an effective protective measure against the transmission of infectious diseases, such as COVID-19, in public spaces.Consequently, certain service providers require clients to wear masks before accessing their services.In this study, a novel facial recognition method is developed to identify individuals wearing medical facemasks in images.The proposed technique combines Convolutional Neural Networks (CNNs) to extract prominent feature characteristics, primarily from the eye and forehead regions of the face, and a facemask classification approach utilizing IInceptionV3, VGG16, VGG19, ResNet50, and MobileNet algorithms.A comparison between the five classifiers is also conducted to determine the most suitable algorithm for two masked face datasets.The VGG19 model outperforms the other models in terms of accuracy for the larger dataset.The proposed method achieves a precision of 98%, an average recall of 98%, an F1_score of 98%, and an overall accuracy of 98%.Therefore, the larger dataset yields higher accuracy, and the overall performance of the models is superior compared to the smaller dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".